We consider data from the Home Credit Default Risk Kaggle challenge with the
objective of \((1 - \mbox{AUROC})\) , and 2 black-box constraints: (i) (deployment) Prediction latency \(t_p\)  enforcing real-time predictions, (ii)
(fairness) Maximum pairwise disparate impact \(d_I\) 
[1]} across all loan applicant age groups enforcing fairness
across groups.
We perform the following experiments: (i) fixing \(d_I = 0.7\) , we vary \(t_p \in [1, 20]\)  (in \(\mu s\) ), and (ii) fixing \(t_p = 10\mu s\)  and we vary
\(d_I \in [0.05, 0.15]\) . Note that the constraints get less
restrictive as the thresholds increase. We apply ADMM to the unconstrained
problem (UCST) and post-hoc filter constraint satisfying pipelines to
demonstrate that these constraints are not trivially satisfied. Then we execute
ADMM with these constraints (CST). Using BO for (REF ) (and
(REF )) & bandits for () (and
(REF )), we get UCST(BO,Ba) & CST(BO,Ba).
